In order to locate targets in an area of interest, radar system transmits pulses and process received echoes reflected by the targets. The echoes can be characterized as a weighted combination of delayed pulses, where complex weights depend on specific target reflectivities. Given the pulses and echoes, radar images can be generated in a range-azimuth plane according to corresponding weights and delays. The azimuth resolution of the radar images depends on a size of an array aperture, and a range resolution depends on a bandwidth of the pulses.
It can be difficult or expensive to construct a large enough aperture to achieve a desired azimuth resolution. Therefore, multiple distributed sensing platforms, each equipped with a relative small aperture array, can be used to collaboratively receive echoes. Benefits of distributed sensing include flexibility of platform placement, low operation and maintenance cost, and a large effective aperture. However, distributed sensing requires more sophisticated signal processing compared to that of a single uniform linear array. Conventional radar imaging methods typically process the echoes received by each sensor platform individually using matched filter. Then, the estimates are combined in a subsequent stage. Generally, the platforms are not uniformly distributed so that the radar images can exhibit annoying artifacts, such as aliancing, ambiguity or ghost, making it difficult to distinguish the targets.
As shown in FIG. 2, in prior art work 2D radar imaging 210 is applied independently to data 201 received by each antenna array 200 to produce a corresponding low resolution 2D radar image 211. The 2D low resolution images aligned and summed 220 to produce 212 a 2D radar image 230 with artifacts, such as aliasing, ambiguity or ghosts.
The performance of the imaging system can be improved using distributed sensing and jointly processing all measurements using methods based on compressive sensing (CS). CS enables accurate reconstruction of signals using a significantly smaller sampling rate compared to the Nyquist rate. The reduction in the sampling rate is achieved by using randomized measurements, improved signal models, and non-linear reconstruction methods, see Candes et al., “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52(2), February 2006. In radar applications, CS can achieve super-resolution images by assuming that the received signal can be modeled as a linear combination of waveforms corresponding to the targets and the underlying vector of target reflectivity is sparse, see Baraniuk et al., “Compressive radar imaging,” IEEE Radar Conference, MA, April 2007, Herman et al., “High-resolution radar via compressed sensing,” IEEE Trans. Signal Process., vol. 57, June 2009, and Potter et al., “Sparsity and compressed sensing in radar imaging,” Proceeding of the IEEE, vol. 98, pp. 1006-1020, June 2010.